Virtual power plant low-carbon optimization scheduling method containing carbon-green evidence interaction and demand response

Abstract

The invention discloses a low-carbon optimal scheduling method for a virtual power plant containing carbon-green certificate interaction and demand response, and belongs to the technical field of power scheduling. According to the method, the combined heat and power generation unit combined operation model coupling carbon capture and electricity-to-gas is introduced into the virtual power plant, so that the consumption of renewable energy sources is promoted, and meanwhile, the carbon emission is reduced; a price-type and substitution-type comprehensive demand response model is introduced to the load side, so that the low-carbon and flexible operation potential of the demand side of the virtual power plant can be fully excavated; a carbon-green certificate combined transaction mechanism is introduced, combination of electricity, carbon and green certificate markets of the virtual power plant is promoted, and economy and environmental protection are considered at the same time. By means of the method, the storage cost of CO2 is avoided to the maximum extent, and the carbon emission of the virtual power plant is reduced; the low-carbon and flexible operation potential of the demand side of the virtual power plant is fully excavated, and the operation cost, the energy purchasing cost and the carbon emission of the virtual power plant are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to a low-carbon optimization dispatching method and system for a virtual power plant involving carbon-green certificate interaction and demand response. Background Art

[0002] Due to the continuous increase in traditional energy consumption, environmental pollution has become increasingly serious. Since then, vigorously developing low-carbon clean energy power generation has become an important measure to maintain the path of sustainable development. In this context, virtual power plants (VPPs) couple various forms of energy and aggregate sources and loads to access the power system, which can fully mobilize supply and demand flexibility resources and improve energy utilization efficiency.

[0003] Carbon Capture System (CCS) and Power-to-Gas (P2G) technologies are effective ways to reduce carbon dioxide emissions from VPPs. CCS can capture a portion of the CO2 produced by coal-fired and gas-fired power plants, while P2G uses this CO2 to react with hydrogen to produce methane. Most current studies have ignored the combined operation mode of CHP units coupled with CCS and P2G in VPPs.

[0004] Most current studies have not built a refined model for the demand response of multiple energy sources on the load side, and the potential of demand-side flexibility resources for VPP low-carbon economic regulation needs to be further explored. With the diversification of energy demand, how to achieve VPP multi-energy complementarity and mobilize load-side flexibility resources has become a problem that needs to be explored.

[0005] Carbon emissions trading (CET) and green certificate trading (GCT) are important means to guide the low-carbon economic operation of VPP. However, most current studies focus on the study of CET or GCT mechanisms alone, ignoring the impact of carbon-green certificate joint trading interaction on the optimal scheduling of VPP low-carbon economy.

[0006] Therefore, the low-carbon optimization scheduling strategy of virtual power plants that considers carbon-green certificate interaction and demand response is an issue that needs to be urgently addressed in this field. Summary of the invention

[0007] The present invention provides a low-carbon optimization scheduling method for a virtual power plant with carbon-green certificate interaction and demand response. By introducing a joint operation model of a cogeneration unit coupling carbon capture and power-to-gas in a virtual power plant, the consumption of renewable energy is promoted while reducing carbon emissions. The introduction of a price-based and substitution-based comprehensive demand response model on the load side can fully tap the low-carbon and flexible operation potential of the demand side of the virtual power plant. The introduction of a carbon-green certificate joint trading mechanism promotes the combination of the electricity, carbon and green certificate markets of virtual power plants, while taking into account both economy and environmental protection.

[0008] The technical solution of the present invention is:

[0009] According to a first aspect of the present invention, a method for low-carbon optimization scheduling of a virtual power plant with carbon-green certificate interaction and demand response is provided, comprising: Step 1, constructing a virtual power plant architecture with an external power grid and a gas grid for energy supply, the internal source mainly including wind power, photovoltaics, power-to-gas, carbon capture, cogeneration units, micro gas turbines, electric refrigerators, and the load side including electric load, electric vehicles, cold load, and hot load; Step 2, based on the virtual power plant architecture, establishing a source-side flexible response model of the virtual power plant and a comprehensive flexible demand response model of the load side of the virtual power plant; wherein the source-side flexible response model of the virtual power plant includes a cogeneration unit model coupling carbon capture and power-to-gas, a micro gas turbine model, an electric refrigerator model, and a virtual power plant The load-side comprehensive flexible demand response model includes a price-based demand response model and an alternative demand response model; Step 3, establish a virtual power plant carbon-green certificate joint trading model; Step 4, based on the virtual power plant source-side flexible response model, the virtual power plant load-side comprehensive flexible demand response model, and the virtual power plant carbon-green certificate joint trading model, construct an objective function for minimizing the comprehensive operating cost of the virtual power plant containing carbon-green certificate interaction and demand response, and construct constraints; under the constraints, the virtual power plant low-carbon economic optimization scheduling objective of the virtual power plant is solved with the minimum comprehensive operating cost of the virtual power plant containing carbon-green certificate interaction and demand response, and the virtual power plant low-carbon optimization scheduling strategy containing carbon-green certificate interaction and demand response is obtained.

[0010] Furthermore, the model expression of the coupled carbon capture and power-to-gas cogeneration unit is:

[0011]

[0012] Where: Net output power of the cogeneration unit at time t; The power supplied to the cogeneration unit at time t; The electric power supplied to the power-to-gas conversion unit by the cogeneration unit at time t; Supply carbon-captured electricity to the cogeneration unit at time t; They are the maximum and minimum power supplied by the cogeneration unit respectively; is the thermal power output of the cogeneration unit at time t; k1 and k2 are the power-to-heat coefficients corresponding to the minimum and maximum power supply of the cogeneration unit respectively; k is the power-to-heat coefficient of the cogeneration unit; Output minimum thermal power for the cogeneration unit; They are the maximum and minimum electric power supplied to the cogeneration unit for power-to-gas conversion; They are the maximum and minimum electric power supplied to the cogeneration unit for carbon capture, respectively; α and β are the coefficients of the power-to-gas efficiency and the amount of carbon dioxide captured by carbon capture, respectively; τ is the conversion coefficient of carbon capture power consumption for carbon capture carbon dioxide capture; They are the maximum and minimum net output power of the cogeneration unit, respectively; is the power output of electricity-to-gas conversion at time t; is the amount of carbon dioxide absorbed by power-to-gas conversion at time t.

[0013] Furthermore, the price-based demand response model is expressed as:

[0014]

[0015] Where: e t,j is the tth row and jth column in the elasticity matrix; ΔE c,t is the change of each type of load at time t after demand response; is the initial value of each type of load at time t; Δe j is the change in the price of each energy source at time j after the demand response; is the initial price of each energy source at time j; ΔE cut,t , ΔE tran,t They are the reduction and transfer changes of each type of load at time t after demand response; are the initial load that can be reduced and transferred at time t; E cut (t,j),E tran (t,j) are the price elasticity matrices of load reduction and transfer for each type; e j is the price of each energy source at time j; among them, various types of loads include electricity, heat and cooling loads, and various types of loads include electricity, heat and cooling loads; T is the scheduling period.

[0016] Furthermore, the alternative demand response model is expressed as:

[0017]

[0018] Where: are the amount of electrical load replaced by the heat load and the amount of electrical load replaced by the cooling load respectively; ε e,h , ε e,c are the substitution coefficients of electric heating and electric cooling, respectively; are the heat and cold loads replaced by the electrical load; υ e ,υ h ,υ c are the unit calorific values ​​of electricity, heat and cold energy respectively; are the utilization rates of electricity, heat and cold energy respectively; are the minimum replacement amounts of replaced electricity, heating and cooling loads respectively; They are the maximum replacement amounts of replaced electricity, heating and cooling loads respectively.

[0019] Furthermore, the virtual power plant carbon-green certificate joint trading model is established, and its green certificate trading expression is:

[0020]

[0021]

[0022] Where: is the green certificate quota of the virtual power plant; k green Green certificate quota coefficient allocated to virtual power plants; is the initial electrical load at time t; The number of green certificates obtained for renewable energy power generation in virtual power plants; green The conversion factor for converting renewable energy power generation into green certificates; are wind power output and photovoltaic output at time t respectively; C GCT is the transaction cost of green certificates; p GCT is the green certificate transaction price; T is the scheduling period.

[0023] Furthermore, the carbon trading expression of the virtual power plant carbon-green certificate joint trading model is as follows:

[0024]

[0025] Where: D VPP , E VPP are the free carbon quota of the virtual power plant, the actual carbon emissions, and the carbon emission rights trading amount actually involved in carbon trading; δ e 、v h are the carbon quota per unit of electricity and heat respectively; a co2 , b co2 、c co2 are the actual carbon emission coefficients of the combined heat and power units; d co2 、e co2 are the actual carbon emission coefficients of the micro gas turbine and purchased electricity respectively; k1 is the electricity-to-heat coefficient corresponding to the minimum power supply of the cogeneration unit; D GREEN Offset carbon emissions for renewable energy access; gis the conversion coefficient; c is the carbon trading base price; λ is the price growth rate; h is the length of the carbon emission interval; are the electrical and thermal power output of the micro gas turbine at time t respectively; is the power purchased from the grid at time t; C CET For carbon trading costs; is the net output power of the cogeneration unit at time t; They are wind power output and photovoltaic output at time t respectively; is the thermal power output of the cogeneration unit at time t; is the amount of carbon dioxide absorbed by power-to-gas conversion at time t; T is the scheduling period.

[0026] Furthermore, the objective function is expressed as:

[0027] min G VPP =C op +C ab +C buy +C CET +C GCT

[0028] Where: C VPP is the total operating cost; C op is the unit operation and maintenance cost; C buy C is the energy purchase cost; ab Penalty cost for abandoning wind power; C CET is the carbon trading cost; C GCT It is the transaction cost of green certificate.

[0029] Furthermore, the unit operation and maintenance cost is expressed as:

[0030]

[0031] Where: c1, c2, c3 are the operation and maintenance cost coefficients of the cogeneration unit; c P2G 、c CCS 、c MT 、c AC are the operation and maintenance cost coefficients of power-to-gas, carbon capture, micro gas turbine, and electric refrigerator, respectively; M is the number of electric vehicles; Net output power of the cogeneration unit at time t; is the thermal power output of the cogeneration unit at time t; The electric power supplied to the power-to-gas conversion unit by the cogeneration unit at time t; Supply carbon-captured electricity to the cogeneration unit at time t; is the electrical power output of the micro gas turbine at time t; The power consumed by the electric refrigerator at time t; is the discharge power of the electric vehicle at time t; ηEV,dis , μ EV are the discharge efficiency and self-loss coefficient of electric vehicles respectively; x EV is the driving distance of electric vehicles.

[0032] Furthermore, the constraints include:

[0033] The power balance constraint is expressed as:

[0034]

[0035] The unit operation constraints are expressed as:

[0036]

[0037] The charging and discharging constraints of electric vehicles are expressed as:

[0038]

[0039]

[0040] Where: They are wind power output and photovoltaic output at time t respectively; is the power purchased from the grid at time t; The power supplied to the cogeneration unit at time t; are the electrical and thermal power output of the micro gas turbine at time t respectively; are the initial electric, heating and cooling loads at time t; ΔP cut,t , ΔH cut,t , ΔC cut,t are the reduction in electricity, heating and cooling loads at time t; ΔP tran,t , ΔH tran,t , ΔC tran,t are the transfer amounts of electricity, heat and cooling loads at time t respectively; They are the amount of electrical load replaced by the heating load and the amount of electrical load replaced by the cooling load respectively; are the heating and cooling loads replaced by the electrical loads, respectively; They are respectively the charging and discharging power of the electric vehicle at time t; The power consumed by the electric refrigerator at time t; is the thermal power output of the cogeneration unit at time t; is the cooling power output of the electric refrigerator at time t; is the cooling power output of the micro gas turbine at time t; They are the maximum and minimum power supplied by the cogeneration unit respectively; The maximum and minimum net output power of the cogeneration unit; They are the maximum and minimum electric power supplied to the cogeneration unit for power-to-gas conversion; Supply the maximum and minimum electric power for carbon capture to the cogeneration unit respectively; Output minimum thermal power for the cogeneration unit; Output the maximum thermal power for the cogeneration unit; They are the upper and lower limits of the micro gas turbine output electric power respectively; They are the upper and lower limits of the micro gas turbine climbing slope, respectively; They are the upper and lower limits of the cooling power output of the electric refrigerator respectively; They are the predicted output of wind power and photovoltaic power at time t respectively; They are wind power and photovoltaic power abandoned at time t respectively; They are the charging and discharging states of the electric vehicle at time t respectively; are the upper limits of electric vehicle charging and discharging respectively; η EV,ch , η EV,dis Charging and discharging efficiency of electric vehicles; S is the charge state of the electric vehicle at time t; ev is the rated capacity of the electric vehicle battery, which is taken as 0.05; They are the upper and lower limits of the state of charge of the electric vehicle respectively; Δt represents each moment.

[0041] According to a second aspect of the present invention, there is provided a virtual power plant low-carbon optimization scheduling system including carbon-green certificate interaction and demand response, comprising: a module including any one of the methods described above.

[0042] The beneficial effects of the present invention are:

[0043] (1) A combined operation mode of CHP units coupled with CCS and P2G is introduced at the VPP source, where CCS captures the CO2 produced by CHP and supplies it to P2G, which not only avoids the storage cost of CO2; but also illustrates through simulation that the carbon emissions of VPP are reduced by 190.47t.

[0044] (2) Introducing a comprehensive demand response strategy of price and substitution on the load side of VPP can fully tap the low-carbon and flexible operation potential of the demand side of VPP; the simulation also illustrates that the method of the present invention reduces the energy purchase cost and carbon emissions by 33.16% and 57.69t respectively.

[0045] (3) The introduction of a carbon-green certificate joint trading mechanism in the VPP promoted the integration of the electricity, carbon and green certificate markets. The simulation also demonstrated that the system operating cost was reduced by RMB 7,100 and carbon emissions by 9.04 t, taking into account both the economic and environmental benefits of the VPP.

[0046] In summary, the method of the present invention not only avoids the storage cost of CO2 to the greatest extent, but also reduces the carbon emissions of VPP; fully taps the low-carbon and flexible operation potential of the VPP demand side, and reduces the VPP operating cost, energy purchase cost and carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of the present invention;

[0048] Figure 2 This is the structure diagram of the virtual power plant;

[0049] Figure 3 Forecast output and electricity, heat and cooling load diagrams for wind and solar power;

[0050] Figure 4 This is the power balance diagram of the electric load in scenario 5;

[0051] Figure 5 This is the power balance diagram of the electric load in scenario 6;

[0052] Figure 6 This is the relationship diagram between electricity price and electricity load change in scenario 1. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily without conflict.

[0054] Example 1: Figure 1-6As shown, a low-carbon optimization scheduling method for a virtual power plant with carbon-green certificate interaction and demand response, including: Step 1, constructing a virtual power plant architecture with an external power grid and gas grid for energy supply, and the internal source measurement mainly includes wind power, photovoltaic, power-to-gas, carbon capture, cogeneration units, micro gas turbines, electric refrigerators, and the load side includes electric load, electric vehicles, cold load, and hot load; Step 2, based on the virtual power plant architecture, establish a virtual power plant source-side flexible response model and a virtual power plant load-side comprehensive flexible demand response model; wherein the virtual power plant source-side flexible response model includes a cogeneration unit model coupled with carbon capture and power-to-gas, a micro gas turbine model, and an electric refrigerator model; the virtual power plant load-side comprehensive flexible demand response model The active demand response model includes a price-based demand response model and an alternative demand response model; Step 3, establish a virtual power plant carbon-green certificate joint trading model; Step 4, based on the virtual power plant source-side flexible response model, the virtual power plant load-side comprehensive flexible demand response model, and the virtual power plant carbon-green certificate joint trading model, construct an objective function for minimizing the comprehensive operating cost of the virtual power plant containing carbon-green certificate interaction and demand response, and construct constraints; under the constraints, the virtual power plant low-carbon economic optimization scheduling objective of the virtual power plant is solved with the minimum comprehensive operating cost of the virtual power plant containing carbon-green certificate interaction and demand response, and the virtual power plant low-carbon optimization scheduling strategy containing carbon-green certificate interaction and demand response is obtained.

[0055] Further, the following is an explanation of the optional specific embodiments of the present invention in combination with experimental data:

[0056] The model expression of the coupled carbon capture and power-to-gas combined heat and power unit is:

[0057]

[0058] The micro gas turbine model expression is:

[0059]

[0060] The electric refrigerator model expression is:

[0061]

[0062] Where: Net output power of the cogeneration unit at time t; The power supplied to the cogeneration unit at time t; The electric power supplied to the power-to-gas conversion unit by the cogeneration unit at time t; Supply carbon captured electric power to the cogeneration unit at time t. The maximum and minimum power supplied by the cogeneration unit are 35 and -15 respectively; are the maximum and minimum net output power of the cogeneration unit, which are 35 and 10 respectively; k1 and k2 are the power-to-heat coefficients corresponding to the minimum and maximum power of the cogeneration unit, which are 0.15 and 0.20 respectively; k is the power-to-heat coefficient of the cogeneration unit, which is 0.85; is the thermal power output of the cogeneration unit at time t; The minimum thermal power output of the cogeneration unit is 0; The maximum and minimum electric powers supplied by the cogeneration unit to convert electricity into gas are 15 and 0 respectively; The maximum and minimum electric power supplied to the cogeneration unit for carbon capture are 10 and 0 respectively; α and β are the coefficients of power-to-gas efficiency and carbon dioxide capture, which are 0.55 and 1.02 respectively; τ is the conversion coefficient of carbon dioxide capture by carbon capture power consumption, which is 0.5; is the power output of electricity-to-gas conversion at time t; is the amount of carbon dioxide absorbed by power-to-gas conversion at time t; are the electrical and thermal power output of the micro gas turbine at time t respectively; is the cooling power output of the micro gas turbine at time t; is the gas power consumed by the micro gas turbine at time t; η e is the micro gas turbine consumption gas conversion electricity coefficient, which is taken as 0.6; η h is the heating coefficient, take 0.95; η y is the waste heat recovery coefficient, which is taken as 0.05; η c is the cooling coefficient, which is taken as 0.95; is the cooling power output of the electric refrigerator; AC is the conversion efficiency of the electric refrigerator, which is taken as 3; is the power consumed by the refrigerator at time t.

[0063] It should be noted that carbon capture can capture part of the CO2 produced by the cogeneration unit and supply it to the power-to-gas, thereby reducing the CO2 emissions of the CHP unit. The operation of the power-to-gas includes two parts: the first part of the power-to-gas uses the electricity provided by the cogeneration unit to electrolyze water to produce hydrogen and oxygen; the second part of the power-to-gas uses the CO2 captured from carbon capture to react with hydrogen to produce natural gas, which not only reduces the system's carbon dioxide emissions, but also reduces the operating costs of purchasing natural gas.

[0064] The price-based demand response model is expressed as:

[0065]

[0066] Where: e t,j is the t-th row and j-th column in the elasticity matrix E(t,j); ΔEc,t is the change of each type of load at time t after demand response; is the initial value of each type of load at time t; Δe j is the change in the price of each energy source at time j after the demand response; is the initial price of each energy source at time j (including They are the reduction and transfer changes of various types of loads at time t after demand response (each type of load includes electricity, heat, and cooling loads, ΔE cut,t Including ΔP cut,t , ΔH cut,t , ΔC cut,t , are the reductions in electricity, heating and cooling loads at time t; ΔE tran,t Including ΔP tran,t , ΔH tran,t , ΔC tran,t , are the transfer amounts of electricity, heat and cooling loads at time t respectively; each energy refers to electricity, heat and cooling energy, corresponding to the load); are the initial load that can be reduced and transferred at time t, which are 0.15 and 0.2 times of the initial value of each type of load respectively; E cut (t,j),E tran (t,j) are the price elasticity matrices of load reduction and transfer for each type; e j is the energy price at time j (including P j , H j , C j ).

[0067] The alternative demand response model is expressed as:

[0068]

[0069]

[0070] Where: are the heating and cooling loads replaced by the electrical loads, respectively; are the amount of electrical load replaced by the heat load and the amount of electrical load replaced by the cooling load respectively; ε e,h , ε e,c are the electric heating and electric cooling substitution coefficients, which are 1.83 and 1.78 respectively; υ e ,υ h ,υ c The unit calorific values ​​of electricity, heat, and cold energy are 3.6MJ, 37MJ, and 10.8MJ respectively; are the utilization rates of electricity, heat, and cold energy, which are 0.85, 0.8, and 0.75 respectively; are the minimum replacement amounts of replaced electricity, heating and cooling loads, respectively, which are taken as 0; are the maximum replacement amounts of replaced electricity, heat and cooling loads, respectively. are the initial electricity, heating and cooling loads at time t respectively.

[0071] It should be noted that price-based demand response means that users adjust their energy usage habits according to the difference in electricity prices at different time periods to achieve the purpose of "peak shaving and valley filling". Substitution-based demand response is an important way to improve user energy flexibility. Users can choose the best energy to meet their energy needs based on the prices of different energy sources at the same time, thereby achieving mutual substitution between different energy sources.

[0072] The virtual power plant carbon-green certificate joint trading model is established, and its green certificate trading expression is:

[0073]

[0074] Where: is the green certificate quota of the virtual power plant; k green The green certificate quota coefficient allocated to virtual power plants is 0.2; is the initial electrical load at time t; The number of green certificates obtained for renewable energy power generation in virtual power plants; green The conversion coefficient of renewable energy power generation to green certificates is 1; are wind power output and photovoltaic output at time t respectively; C GCT is the transaction cost of green certificates; p GCT The transaction price of green certificate is 50 yuan.

[0075] The carbon trading expression of the virtual power plant carbon-green certificate joint trading model is as follows:

[0076]

[0077]

[0078] Where: D VPP , E VPP are the free carbon quota of the virtual power plant, the actual carbon emissions, and the carbon emission rights trading amount actually involved in carbon trading; δ e , δ h are the carbon quota per unit of electricity and heat, which are 0.798 and 0.385 respectively; a co2 , b co2 、c co2 are the actual carbon emission coefficients of the cogeneration units, which are 0.89, 0.0017, and 26.15 respectively; d co2 、e co2are the actual carbon emission coefficients of the micro gas turbine and purchased electricity, which are 1.03 and 1.08 respectively; D GREEN Offset carbon emissions for renewable energy access; g is the conversion coefficient, which is 0.08; c is the carbon trading base price, which is 100 yuan / t; λ is the price growth rate, which is 0.1; h is the length of the carbon emission interval, which is 10t; are the electrical and thermal power output of the micro gas turbine at time t respectively; is the power purchased from the grid at time t; C CET For carbon trading costs; is the net output power of the cogeneration unit at time t; They are wind power output and photovoltaic output at time t respectively; is the thermal power output of the cogeneration unit at time t; is the amount of carbon dioxide absorbed by power-to-gas conversion at time t; T is 24.

[0079] It should be noted that the green certificate trading mechanism refers to a certificate issued by the regulatory authorities for the amount of electricity generated by new energy sources and the amount of green electricity consumed by users. The carbon trading mechanism refers to the reasonable production of enterprises based on the carbon emission quotas allocated by the regulatory authorities in order to achieve the purpose of reducing carbon emissions. If the actual carbon emissions of the VPP are lower than the allocated carbon quota, the remaining carbon quota can be sold through the carbon trading market to benefit, otherwise it needs to purchase the corresponding carbon quota.

[0080] The objective function is expressed as:

[0081] min C VPP =C op +C ab +C buy +C CET +C GCT

[0082] Where: C VPP is the total operating cost; C op is the unit operation and maintenance cost; C buy C is the energy purchase cost; ab Penalty cost for abandoning wind power; C CET is the carbon trading cost; C GCT It is the transaction cost of green certificate.

[0083] The specific calculation model is as follows:

[0084]

[0085] Where: c1, c2, c3 are the operation and maintenance cost coefficients of the cogeneration unit, which are 13.29, 0.004, and 39 respectively; c P2G 、cCCS 、c MT 、c AC are the operation and maintenance cost coefficients of power-to-gas, carbon capture, micro gas turbine, and electric refrigerator, which are 22, 22, 60, and 90 respectively; M is the number of electric vehicles, which is 1000; Net output power of the cogeneration unit at time t; is the thermal power output of the cogeneration unit at time t; The electric power supplied to the power-to-gas conversion unit by the cogeneration unit at time t; Supply carbon-captured electricity to the cogeneration unit at time t; is the electrical power output of the micro gas turbine at time t; The power consumed by the electric refrigerator at time t; is the discharge power of the electric vehicle at time t; η EV,dis , μ EV are the discharge efficiency and self-loss coefficient of electric vehicles, which are 0.9 and 0.1 respectively; x EV is the driving distance of electric vehicles;

[0086]

[0087] Where: c WT 、c PV are the penalty cost coefficients for wind and solar abandonment, which are 0.25 and 0.2 respectively; are the abandoned wind power and photovoltaic power at time t respectively.

[0088]

[0089] Where: is the power purchased from the grid at time t; The power of gas purchased from the gas network at time t; are the electricity and gas purchase prices at time t respectively.

[0090] The constraints include:

[0091] The power balance constraint is expressed as:

[0092]

[0093] The unit operation constraints are expressed as:

[0094]

[0095] The charging and discharging constraints of electric vehicles are expressed as:

[0096]

[0097]

[0098] Where: They are wind power output and photovoltaic output at time t respectively; is the power purchased from the grid at time t; The power supplied to the cogeneration unit at time t; are the electrical and thermal power output of the micro gas turbine at time t respectively; are the initial electric, heating and cooling loads at time t; ΔP cut,t , ΔH cut,t , ΔC cut,t are the reduction in electricity, heating and cooling loads at time t; ΔP tran,t , ΔH tran,t , ΔC tran,t are the transfer amounts of electricity, heat and cooling loads at time t respectively; They are the amount of electrical load replaced by the heating load and the amount of electrical load replaced by the cooling load respectively; are the heating and cooling loads replaced by the electrical loads, respectively; They are respectively the charging and discharging power of the electric vehicle at time t; The power consumed by the electric refrigerator at time t; is the thermal power output of the cogeneration unit at time t; is the cooling power output of the electric refrigerator at time t; is the cooling power output of the micro gas turbine at time t; The minimum thermal power output of the cogeneration unit is 0; The maximum thermal power output of the cogeneration unit is 40; are the upper and lower limits of the micro gas turbine output power, which are 30 and 5 respectively; They are the upper and lower limits of the micro gas turbine climbing slope, which are 20 and -20 respectively; They are the upper and lower limits of the cooling power output of the electric refrigerator, which are 4 and 0 respectively; They are the predicted output of wind power and photovoltaic power at time t respectively; are wind power and photovoltaic power abandoned at time t respectively; η EV,ch , η EV,dis is the charging and discharging efficiency of electric vehicles, which are 0.9 and 0.9; They are the charging and discharging states of the electric vehicle at time t respectively; They are the upper limits of electric vehicle charging and discharging, which are 0.057 and 0.057 respectively; S is the charge state of the electric vehicle at time t; ev is the rated capacity of the electric vehicle battery, which is taken as 0.05; They are the upper and lower limits of the state of charge of the electric vehicle, which are 0.9 and 0.2 respectively; Δt represents each moment, which is 1h.

[0099] Furthermore, in order to analyze the impact of the proposed model on carbon emissions and economic benefits of low-carbon optimized scheduling of virtual power plants containing carbon-green certificates and demand response, the present invention sets up 6 scenarios for comparative analysis, namely:

[0100] Scenario 1: Low-carbon optimization scheduling strategy of virtual power plants with carbon-green certificates and demand response, i.e. the method proposed in the present invention;

[0101] Scenario 2: Based on Scenario 1, the carbon-green certificate joint trading model is not considered;

[0102] Scenario 3: Based on Scenario 1, carbon capture and carbon trading are not considered;

[0103] Scenario 4: Based on Scenario 1, carbon trading is not considered;

[0104] Scenario 5: Based on Scenario 1, price-based and alternative demand response models are not considered;

[0105] Scenario 6: Based on Scenario 1, price-based and substitution-based demand response models and electric vehicle charging and discharging constraints are not considered;

[0106] According to the above six scenarios, the dispatch results of the virtual power plant are analyzed.

[0107] In order to verify the effectiveness and applicability of the proposed model, the present invention uses 24 hours as a cycle and 1 hour as a step to solve the problem. Solution process:

[0108] 1) Input original data and parameters, including wind power forecast output Photovoltaic output forecast Initial electrical load Initial heat load Initial cooling load Time-of-use electricity price Gas purchase price Data. Figure 3 , as shown in Table 1.

[0109] Table 1 Energy prices

[0110]

[0111] 2) Input variables And meet the model of the combined heat and power unit with coupled carbon capture and power-to-gas, the input variables And meet the micro gas turbine model, input variables And satisfy the electric refrigerator model.

[0112] 3) Input variable ΔE c,t , ΔEcut,t , ΔE tran,t And satisfy the price-based demand response model, the input variables and meet alternative demand response models.

[0113] 4) Input variables And satisfy the green certificate trading model, input variables And meet the carbon trading model.

[0114] 5) Input variables And build with operation and maintenance cost C op , Penalty cost for abandoning wind and solar power C ab , Purchase energy cost C buy 、Carbon trading costs C CET 、Green certificate transaction cost C GCT Total operating cost C VPP The minimum objective function satisfies the constraints including power balance, unit operation, and electric vehicle charging and discharging.

[0115] 6) Using a 24-hour period and a 1-hour step length, the CPLEX solver is used to solve the output values ​​of the above variables, and on this basis, Δe is solved. j , C GCT , D VPP , e VPP , C CET , C ab , C buy , C op The total operating cost C is obtained VPP Finally, a low-carbon optimization scheduling strategy for virtual power plants with carbon-green certificate interaction and demand response is obtained.

[0116] The comparison of virtual power plant dispatch results under different scenarios is shown in Table 2.

[0117] Table 2 Comparison of virtual power plant dispatch results under different scenarios

[0118]

[0119] As shown in Table 2, compared with scenario 1, the carbon emissions and carbon trading costs of scenario 2 increased by 9.04t and 6560.06 yuan respectively, and the total operating costs increased by 7,100 yuan. This is because when the carbon-green certificate joint trading is in operation in scenario 1, VPP offsets part of the carbon emission reduction by obtaining green certificates in the carbon trading market, which greatly reduces the carbon trading costs, reflecting the low-carbon economy of the carbon-green certificate joint trading mechanism. Compared with scenario 4, the carbon emissions and green certificate costs of scenario 3 increased by 190.47t and 2621.95 yuan respectively. This is because when the cogeneration unit coupling carbon capture and power-to-gas is in joint operation in scenario 4, carbon capture can capture part of the CO2 generated by the cogeneration unit and provide it to power-to-gas to produce natural gas, which not only reduces the external energy purchase cost of the virtual power plant (VPP) by 15,200 yuan, but also reduces carbon emissions. Compared with Scenario 1, the carbon emissions and operating costs of Scenario 5 increased by 57.69 tons and 2,100 yuan respectively. This is because the consideration of price-based and alternative demand responses can better guide users to change their energy strategies, taking into account the economy and low carbon of VPP. Compared with Scenario 6, the carbon trading cost and green certificate trading cost of Scenario 5 decreased by 891.18 yuan and 618.36 yuan respectively. This is because the introduction of electric vehicles in VPP can store unused energy during the period of high wind and solar power generation, and release it during the period of low wind and solar power generation to meet the load energy needs, which not only reduces the CO2 released by the output of cogeneration units and micro gas turbine units, but also increases the utilization rate of wind and solar power.

[0120] In summary, the introduction of a carbon-green certificate joint trading mechanism in VPP can promote the coupling of electricity, carbon, and green certificate markets, taking into account both system economy and low carbon. Secondly, the operation mode of the combined heat and power unit coupled with carbon capture and power-to-gas can effectively improve the utilization rate of wind and solar power in VPP and reduce carbon emissions. At the same time, considering electric vehicles and demand response, by utilizing load-side flexibility resources, the carbon emissions and operating costs of VPP are further reduced, verifying the effectiveness of the proposed low-carbon optimization scheduling strategy for virtual power plants containing carbon-green certificates and demand response.

[0121] The power balance diagram of the electric load in scenario 1 and scenario 6 is as follows Figure 4 , 5 As shown. Compared with scenario 6, scenario 1 has enhanced the source-load coordination of VPP and made the energy supply mode of the system more flexible after the introduction of electric vehicles and comprehensive demand response for electricity, heat and cooling. During the period of abundant wind and solar power, electric vehicles can store the unused abandoned wind and solar power, and release them during the period of low wind and solar power generation to meet the energy demand on the load side, reducing the energy purchase cost of VPP. At the same time, the load curve changes more obviously with the wind and solar power output, further reducing the carbon emissions of VPP.

[0122] Scenario 1: The relationship between electricity price and electricity load is as follows Figure 6As shown. During the peak electricity price period (11:00-15:00, 18:00-21:00), the price-based load response demand response strategy reduces part of the electricity load and transfers a large amount of peak load to the low electricity price period (01:00-07:00) to achieve the purpose of "peak shaving and valley filling"; when the electricity price is much higher than the heating and cooling prices (08:00-24:00), part of the electricity load is replaced by heating and cooling loads to reduce the operating cost of the system. Figure 5 It can be seen that when price-based and alternative demand responses work together, the load curve after the response follows the wind and solar power output more obviously, and the load curve is also smoother, which can better promote the consumption of renewable energy and achieve peak shaving and valley filling.

[0123] Embodiment 2: A virtual power plant low-carbon optimization scheduling system with carbon-green certificate interaction and demand response, including: a construction module for constructing a virtual power plant architecture with an external power grid and gas grid for energy supply, and an internal source measurement including wind power, photovoltaics, power-to-gas, carbon capture, cogeneration units, micro gas turbines, electric refrigerators, and a load side including electric load, electric vehicles, cold load, and hot load; a first establishment module for establishing a virtual power plant source-side flexible response model and a virtual power plant load-side comprehensive flexible demand response model based on the virtual power plant architecture; wherein the virtual power plant source-side flexible response model includes a cogeneration unit model coupled with carbon capture and power-to-gas, a micro gas turbine model, an electric refrigerator model, and a virtual power plant load-side comprehensive flexible demand response model. The flexible demand response model includes a price-based demand response model and an alternative demand response model; the second establishment module is used to establish a virtual power plant carbon-green certificate joint trading model; the scheduling module is used to construct an objective function for minimizing the comprehensive operating cost of the virtual power plant containing carbon-green certificate interaction and demand response based on the virtual power plant source-side flexible response model, the virtual power plant load-side comprehensive flexible demand response model, and the virtual power plant carbon-green certificate joint trading model, and construct constraints; under the constraints, the virtual power plant low-carbon economic optimization scheduling target is solved with the minimum comprehensive operating cost of the virtual power plant containing carbon-green certificate interaction and demand response, and the virtual power plant low-carbon optimization scheduling strategy containing carbon-green certificate interaction and demand response is obtained.

[0124] Embodiment 3: A virtual power plant system comprises an electronic device, wherein the electronic device comprises a processor and a memory for storing programs executable by the processor, and when the processor executes the program stored in the memory, the low-carbon optimization scheduling method for a virtual power plant with carbon-green certificate interaction and demand response as described in any one of Embodiment 1 is implemented.

[0125] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0126] Although specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the spirit of the present invention.

Claims

1. A low-carbon optimization scheduling method for a virtual power plant with carbon-green certificate interaction and demand response, characterized in that: include: Step 1: Build a virtual power plant architecture with external power grid and gas grid for energy supply, internal power supply mainly includes wind power, photovoltaic, power-to-gas, carbon capture, cogeneration units, micro gas turbines, electric refrigerators, and load side including electric load, electric vehicles, cooling load, and heating load; Step 2. Based on the virtual power plant architecture, establish a flexible response model on the source side of the virtual power plant and a comprehensive flexible demand response model on the load side of the virtual power plant; the flexible response model on the source side of the virtual power plant includes a cogeneration unit model of coupled carbon capture and power-to-gas, a micro gas turbine model, and an electric refrigerator model; the comprehensive flexible demand response model on the load side of the virtual power plant includes a price-based demand response model and a substitution-based demand response model; Step 3. Establish a virtual power plant carbon-green certificate joint trading model; Step 4. Based on the source-side flexible response model of the virtual power plant, the load-side comprehensive flexible demand response model of the virtual power plant, and the carbon-green certificate joint trading model of the virtual power plant, construct an objective function for minimizing the comprehensive operating cost of the virtual power plant containing carbon-green certificate interaction and demand response, and construct constraints; under the constraints, the virtual power plant low-carbon economic optimization scheduling objective of minimizing the comprehensive operating cost of the virtual power plant containing carbon-green certificate interaction and demand response is solved to obtain a low-carbon optimization scheduling strategy for the virtual power plant containing carbon-green certificate interaction and demand response.

2. The low-carbon optimization scheduling method for virtual power plants with carbon-green certificate interaction and demand response according to claim 1 is characterized in that: The model expression of the coupled carbon capture and power-to-gas combined heat and power unit is: Where: Net output power of the cogeneration unit at time t; The power supplied to the cogeneration unit at time t; The electric power supplied to the power-to-gas conversion unit by the cogeneration unit at time t; Supply carbon-captured electricity to the cogeneration unit at time t; They are the maximum and minimum power supplied by the cogeneration unit respectively; is the thermal power output of the cogeneration unit at time t; k1 and k2 are the power-to-heat coefficients corresponding to the minimum and maximum power supply of the cogeneration unit respectively; k is the power-to-heat coefficient of the cogeneration unit; Output minimum thermal power for the cogeneration unit; They are the maximum and minimum electric power supplied to the cogeneration unit for power-to-gas conversion; They are the maximum and minimum electric power supplied to the cogeneration unit for carbon capture, respectively; α and β are the coefficients of power-to-gas efficiency and carbon dioxide capture, respectively; τ is the carbon capture power consumption carbon dioxide capture conversion coefficient; They are the maximum and minimum net output power of the cogeneration unit, respectively; is the power output of electricity-to-gas conversion at time t; is the amount of carbon dioxide absorbed by power-to-gas conversion at time t.

3. The low-carbon optimization scheduling method for virtual power plants with carbon-green certificate interaction and demand response according to claim 1 is characterized in that: The price-based demand response model is expressed as: Where: e t,j is the tth row and jth column in the elasticity matrix; ΔE c,t is the change of each type of load at time t after demand response; is the initial value of each type of load at time t; Δe j is the change in the price of each energy source at time j after the demand response; is the initial price of each energy source at time j; ΔE cut,t , ΔE tran,t They are the reduction and transfer changes of each type of load at time t after demand response; are the initial load that can be reduced and transferred at time t; E cut (t,j),E tran (t,j) are the price elasticity matrices of load reduction and transfer for each type; e j is the price of each energy source at time j; among them, various types of loads include electricity, heat and cooling loads, and various types of loads include electricity, heat and cooling loads; T is the scheduling period.

4. The low-carbon optimization scheduling method for virtual power plants with carbon-green certificate interaction and demand response according to claim 1 is characterized in that: The alternative demand response model is expressed as: Where: are the amount of electrical load replaced by the heat load and the amount of electrical load replaced by the cooling load respectively; ε e,h , ε e,c are the substitution coefficients of electric heating and electric cooling, respectively; are the heat and cold loads replaced by the electrical load; υ e ,υ h ,υ c are the unit calorific values ​​of electricity, heat and cold energy respectively; are the utilization rates of electricity, heat and cold energy respectively; are the minimum replacement amounts of replaced electricity, heating and cooling loads respectively; They are the maximum replacement amounts of replaced electricity, heating and cooling loads respectively.

5. The low-carbon optimization scheduling method for virtual power plants with carbon-green certificate interaction and demand response according to claim 1 is characterized in that: The virtual power plant carbon-green certificate joint trading model is established, and its green certificate trading expression is: Where: is the green certificate quota of the virtual power plant; k green Green certificate quota coefficient allocated to virtual power plants; is the initial electrical load at time t; The number of green certificates obtained for renewable energy power generation in virtual power plants; green The conversion factor for converting renewable energy power generation into green certificates; are wind power output and photovoltaic output at time t respectively; C GCT is the transaction cost of green certificates; p GCT is the green certificate transaction price; T is the scheduling period.

6. The low-carbon optimization scheduling method for virtual power plants with carbon-green certificate interaction and demand response according to claim 1 is characterized in that: The carbon trading expression of the virtual power plant carbon-green certificate joint trading model is as follows: Where: D VPP , E VPP are the free carbon quota of the virtual power plant, the actual carbon emissions, and the carbon emission rights trading amount actually involved in carbon trading; δ e , δ h are the carbon quota per unit of electricity and heat respectively; a co2 、b co2 、c co2 are the actual carbon emission coefficients of the cogeneration units respectively; d co2 、e co2 are the actual carbon emission coefficients of the micro gas turbine and purchased electricity, respectively; k1 is the electricity-to-heat coefficient corresponding to the minimum power supply of the cogeneration unit; D GREEN Offset carbon emissions for renewable energy access; g is the conversion coefficient; c is the carbon trading base price; λ is the price growth rate; h is the length of the carbon emission interval; are the electrical and thermal power output of the micro gas turbine at time t respectively; is the power purchased from the grid at time t; C CET For carbon trading costs; is the net output power of the cogeneration unit at time t; They are wind power output and photovoltaic output at time t respectively; is the thermal power output of the cogeneration unit at time t; is the amount of carbon dioxide absorbed by power-to-gas conversion at time t; T is the scheduling period.

7. The low-carbon optimization scheduling method for virtual power plants with carbon-green certificate interaction and demand response according to claim 1 is characterized in that: The objective function is expressed as: min C VPP =C op +C ab +C buy +C CET +C GCT Where: C VPP is the total operating cost; C op is the unit operation and maintenance cost; C buy C is the energy purchase cost; ab Penalty cost for abandoning wind power; C CET is the carbon trading cost; C GCT It is the transaction cost of green certificate.

8. The low-carbon optimization scheduling method for virtual power plants with carbon-green certificate interaction and demand response according to claim 7 is characterized in that: The unit operation and maintenance cost is expressed as: Where: c1, c2, c3 are the operation and maintenance cost coefficients of the cogeneration unit; c P2G 、c CCS 、c MT 、c AC are the operation and maintenance cost coefficients of power-to-gas, carbon capture, micro gas turbine, and electric refrigerator, respectively; M is the number of electric vehicles; Net output power of the cogeneration unit at time t; is the thermal power output of the cogeneration unit at time t; The electric power supplied to the power-to-gas conversion unit by the cogeneration unit at time t; Supply carbon-captured electricity to the cogeneration unit at time t; is the electrical power output of the micro gas turbine at time t; The power consumed by the electric refrigerator at time t; is the discharge power of the electric vehicle at time t; η EV,dis , μ EV are the discharge efficiency and self-loss coefficient of electric vehicles respectively; x EV is the driving distance of electric vehicles.

9. The low-carbon optimization scheduling method for virtual power plants with carbon-green certificate interaction and demand response according to claim 1 is characterized in that: The constraints include: The power balance constraint is expressed as: The unit operation constraints are expressed as: The charging and discharging constraints of electric vehicles are expressed as: Where: They are wind power output and photovoltaic output at time t respectively; is the power purchased from the grid at time t; The power supplied to the cogeneration unit at time t; are the electrical and thermal power output of the micro gas turbine at time t respectively; are the initial electric, heating and cooling loads at time t; ΔP cut,t , ΔH cut,t , ΔC cut,t are the reduction in electricity, heating and cooling loads at time t; ΔP tran,t , ΔH tran,s , ΔC tran,t are the transfer amounts of electricity, heat and cooling loads at time t respectively; They are the amount of electrical load replaced by the heating load and the amount of electrical load replaced by the cooling load respectively; are the heating and cooling loads replaced by the electrical loads, respectively; They are respectively the charging and discharging power of the electric vehicle at time t; The power consumed by the electric refrigerator at time t; is the thermal power output of the cogeneration unit at time t; is the cooling power output of the electric refrigerator at time t; is the cooling power output of the micro gas turbine at time t; They are the maximum and minimum power supplied by the cogeneration unit respectively; The maximum and minimum net output power of the cogeneration unit; They are the maximum and minimum electric power supplied to the cogeneration unit for power-to-gas conversion; Supply the maximum and minimum electric power for carbon capture to the cogeneration unit respectively; Output minimum thermal power for the cogeneration unit; Output the maximum thermal power for the cogeneration unit; They are the upper and lower limits of the micro gas turbine output electric power respectively; They are the upper and lower limits of the micro gas turbine climbing slope, respectively; They are the upper and lower limits of the cooling power output of the electric refrigerator respectively; They are the predicted output of wind power and photovoltaic power at time t respectively; They are wind power and photovoltaic power abandoned at time t respectively; They are the charging and discharging states of the electric vehicle at time t respectively; are the upper limits of electric vehicle charging and discharging respectively; η EV,ch , η EV,dis Charging and discharging efficiency of electric vehicles; S is the charge state of the electric vehicle at time t; ev is the rated capacity of the electric vehicle battery, which is taken as 0.05; They are the upper and lower limits of the state of charge of the electric vehicle respectively; Δt represents each moment.

10. A virtual power plant low-carbon optimization scheduling system with carbon-green certificate interaction and demand response, characterized in that: include: A module comprising the method according to any one of claims 1 to 9.

Images